Create a MindSphere connection to monitor the Cyber Physical Lab process timings. The data will be gathered from different sensors on the physical model and these data will then be sent to the MindSphere. Then a Mendix program needs to be created to calculate the process time
• Extract the entry or the exit signals from the process station (Use Node-Red to generate data like an infrared sensor sensed a moving object on a conveyor belt, or generate data from a Raspberry Pi if available)
• Push the gathered data to the MindSphere cloud
• Build a Mendix app to analyse and present the process timings
If you have a Raspberry Pi and two inductive proximity sensors you can consider working on this:
Develop a basic hardware test rig in order to simulate the data that we will eventually receive from the digital twins. This test rig will consist of a Raspberry Pi that will control two inductive proximity sensors. The process of moving an object past the sensors will be used to imitate a manufactured part passing through the stations in the virtual models, and as such, the readings taken by these sensors can be used to imitate the entry and exit signal data that will be obtained from the final digital twins. These readings will be extracted into MindSphere as an asset, which is significant as it will allow the entire MindSphere sub-group to gain experience with exporting data into MindSphere from an external source. This experience will allow us to more easily obtain and transfer the signal data from the final digital twins upon their completion. To verify this objective we must ensure that the signal data is being sent accurately, and so we can organize trials where we compare the data readings taken by the Raspberry Pi to physical readings to verify that the measurements are appropriate. We must also ensure that the signal data is being held in the correct format such that it can be processed appropriately in the next objective. This will be verified by inspecting the sensor data using the asset manager.
Once the test rig has been developed, and the signal data has been extracted into Mindsphere, the next objective will be to export this data to a Mendix application. This application will be deployed using a managed runtime environment powered by Cloud Foundry. It will be developed using the low-code development tools available in Mendix, and will take the form of a mobile app. Once the signal data has been transferred to Mendix, the difference between the two sensor readings can be calculated to simulate the calculation of the digital twin process timings. These simulated process timings will be displayed on the mobile app, and several iterations of the app are expected to be developed as the group gain more experience with the software and better judgement as to how this information can be represented most appropriately. One way in which we must verify this objective is to ensure that the correct calculation has been implemented to find the difference between the two sensor readings. We can manually complete the calculations based on the sensor readings to prove that this is being carried out accurately. We must also ensure that the Mendix app uses the MindSphere API to import the time series data from MindSphere, as to meet the appropriate technical requirement.
This is the overall concept. We only need to work on the Node-red to MindSphere to Mendix part or MindSphere to Mendix part if you have a raspberry pi and two inductive proximity sensors.
Technical Requirements you might want to know
Build a Mindsphere App in mendix:
Deploying Mendix to MindSphere:
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